Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations200000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.1 MiB
Average record size in memory200.0 B

Variable types

Numeric16
Categorical9

Alerts

bathrooms is highly overall correlated with roomsHigh correlation
city is highly overall correlated with countryHigh correlation
country is highly overall correlated with cityHigh correlation
customer_salary is highly overall correlated with emi_to_income_ratioHigh correlation
decision is highly overall correlated with satisfaction_scoreHigh correlation
down_payment is highly overall correlated with loan_amount and 2 other fieldsHigh correlation
emi_to_income_ratio is highly overall correlated with customer_salary and 3 other fieldsHigh correlation
loan_amount is highly overall correlated with down_payment and 3 other fieldsHigh correlation
price is highly overall correlated with down_payment and 3 other fieldsHigh correlation
property_size_sqft is highly overall correlated with down_payment and 3 other fieldsHigh correlation
rooms is highly overall correlated with bathroomsHigh correlation
satisfaction_score is highly overall correlated with decisionHigh correlation
property_id is uniformly distributedUniform
property_id has unique valuesUnique
previous_owners has 28648 (14.3%) zerosZeros
crime_cases_reported has 62909 (31.5%) zerosZeros

Reproduction

Analysis started2025-11-11 00:27:45.189169
Analysis finished2025-11-11 00:28:44.127918
Duration58.94 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

property_id
Real number (ℝ)

Uniform  Unique 

Distinct200000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100000.5
Minimum1
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:44.282509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10000.95
Q150000.75
median100000.5
Q3150000.25
95-th percentile190000.05
Maximum200000
Range199999
Interquartile range (IQR)99999.5

Descriptive statistics

Standard deviation57735.171
Coefficient of variation (CV)0.57734883
Kurtosis-1.2
Mean100000.5
Median Absolute Deviation (MAD)50000
Skewness0
Sum2.00001 × 1010
Variance3.33335 × 109
MonotonicityStrictly increasing
2025-11-11T00:28:44.467801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999841
 
< 0.1%
1999831
 
< 0.1%
1999821
 
< 0.1%
1999811
 
< 0.1%
1999801
 
< 0.1%
1999791
 
< 0.1%
1999781
 
< 0.1%
1999771
 
< 0.1%
1999761
 
< 0.1%
1999751
 
< 0.1%
Other values (199990)199990
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
2000001
< 0.1%
1999991
< 0.1%
1999981
< 0.1%
1999971
< 0.1%
1999961
< 0.1%
1999951
< 0.1%
1999941
< 0.1%
1999931
< 0.1%
1999921
< 0.1%
1999911
< 0.1%

country
Categorical

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
France
15628 
China
15536 
Australia
15442 
UK
15413 
Germany
15408 
Other values (8)
122573 

Length

Max length12
Median length7
Mean length6.00423
Min length2

Characters and Unicode

Total characters1200846
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSouth Africa
3rd rowSouth Africa
4th rowGermany
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
France15628
 
7.8%
China15536
 
7.8%
Australia15442
 
7.7%
UK15413
 
7.7%
Germany15408
 
7.7%
South Africa15401
 
7.7%
Canada15401
 
7.7%
Brazil15397
 
7.7%
India15357
 
7.7%
Japan15317
 
7.7%
Other values (3)45700
22.9%

Length

2025-11-11T00:28:44.622705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
france15628
 
7.3%
china15536
 
7.2%
australia15442
 
7.2%
uk15413
 
7.2%
germany15408
 
7.2%
south15401
 
7.1%
africa15401
 
7.1%
canada15401
 
7.1%
brazil15397
 
7.1%
india15357
 
7.1%
Other values (4)61017
28.3%

Most occurring characters

ValueCountFrequency (%)
a215726
18.0%
n107925
 
9.0%
r92554
 
7.7%
i92411
 
7.7%
A61265
 
5.1%
e46314
 
3.9%
S45960
 
3.8%
U45835
 
3.8%
c31029
 
2.6%
h30937
 
2.6%
Other values (21)430890
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1200846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a215726
18.0%
n107925
 
9.0%
r92554
 
7.7%
i92411
 
7.7%
A61265
 
5.1%
e46314
 
3.9%
S45960
 
3.8%
U45835
 
3.8%
c31029
 
2.6%
h30937
 
2.6%
Other values (21)430890
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1200846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a215726
18.0%
n107925
 
9.0%
r92554
 
7.7%
i92411
 
7.7%
A61265
 
5.1%
e46314
 
3.9%
S45960
 
3.8%
U45835
 
3.8%
c31029
 
2.6%
h30937
 
2.6%
Other values (21)430890
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1200846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a215726
18.0%
n107925
 
9.0%
r92554
 
7.7%
i92411
 
7.7%
A61265
 
5.1%
e46314
 
3.9%
S45960
 
3.8%
U45835
 
3.8%
c31029
 
2.6%
h30937
 
2.6%
Other values (21)430890
35.9%

city
Categorical

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Singapore
 
15278
São Paulo
 
7755
Johannesburg
 
7712
Cape Town
 
7689
Rio de Janeiro
 
7642
Other values (35)
153924 

Length

Max length14
Median length12
Mean length7.995205
Min length4

Characters and Unicode

Total characters1599041
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarseille
2nd rowCape Town
3rd rowJohannesburg
4th rowFrankfurt
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Singapore15278
 
7.6%
São Paulo7755
 
3.9%
Johannesburg7712
 
3.9%
Cape Town7689
 
3.8%
Rio de Janeiro7642
 
3.8%
Dubai7637
 
3.8%
Abu Dhabi7504
 
3.8%
Marseille5328
 
2.7%
Beijing5328
 
2.7%
Melbourne5289
 
2.6%
Other values (30)122838
61.4%

Length

2025-11-11T00:28:44.766285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
singapore15278
 
6.2%
são7755
 
3.1%
paulo7755
 
3.1%
johannesburg7712
 
3.1%
cape7689
 
3.1%
town7689
 
3.1%
rio7642
 
3.1%
de7642
 
3.1%
janeiro7642
 
3.1%
dubai7637
 
3.1%
Other values (38)162966
65.9%

Most occurring characters

ValueCountFrequency (%)
n155848
 
9.7%
o154290
 
9.6%
a144968
 
9.1%
e141558
 
8.9%
i112800
 
7.1%
r105094
 
6.6%
u59390
 
3.7%
h56610
 
3.5%
47407
 
3.0%
g46148
 
2.9%
Other values (34)574928
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1599041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n155848
 
9.7%
o154290
 
9.6%
a144968
 
9.1%
e141558
 
8.9%
i112800
 
7.1%
r105094
 
6.6%
u59390
 
3.7%
h56610
 
3.5%
47407
 
3.0%
g46148
 
2.9%
Other values (34)574928
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1599041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n155848
 
9.7%
o154290
 
9.6%
a144968
 
9.1%
e141558
 
8.9%
i112800
 
7.1%
r105094
 
6.6%
u59390
 
3.7%
h56610
 
3.5%
47407
 
3.0%
g46148
 
2.9%
Other values (34)574928
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1599041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n155848
 
9.7%
o154290
 
9.6%
a144968
 
9.1%
e141558
 
8.9%
i112800
 
7.1%
r105094
 
6.6%
u59390
 
3.7%
h56610
 
3.5%
47407
 
3.0%
g46148
 
2.9%
Other values (34)574928
36.0%

property_type
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Farmhouse
33518 
Apartment
33398 
Townhouse
33395 
Villa
33347 
Independent House
33334 

Length

Max length17
Median length9
Mean length9.1713
Min length5

Characters and Unicode

Total characters1834260
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFarmhouse
2nd rowApartment
3rd rowFarmhouse
4th rowFarmhouse
5th rowTownhouse

Common Values

ValueCountFrequency (%)
Farmhouse33518
16.8%
Apartment33398
16.7%
Townhouse33395
16.7%
Villa33347
16.7%
Independent House33334
16.7%
Studio33008
16.5%

Length

2025-11-11T00:28:44.899848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:45.037007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
farmhouse33518
14.4%
apartment33398
14.3%
townhouse33395
14.3%
villa33347
14.3%
independent33334
14.3%
house33334
14.3%
studio33008
14.1%

Most occurring characters

ValueCountFrequency (%)
e233647
12.7%
n166795
 
9.1%
o166650
 
9.1%
u133255
 
7.3%
t133138
 
7.3%
a100263
 
5.5%
s100247
 
5.5%
d99676
 
5.4%
m66916
 
3.6%
r66916
 
3.6%
Other values (13)566757
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1834260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e233647
12.7%
n166795
 
9.1%
o166650
 
9.1%
u133255
 
7.3%
t133138
 
7.3%
a100263
 
5.5%
s100247
 
5.5%
d99676
 
5.4%
m66916
 
3.6%
r66916
 
3.6%
Other values (13)566757
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1834260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e233647
12.7%
n166795
 
9.1%
o166650
 
9.1%
u133255
 
7.3%
t133138
 
7.3%
a100263
 
5.5%
s100247
 
5.5%
d99676
 
5.4%
m66916
 
3.6%
r66916
 
3.6%
Other values (13)566757
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1834260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e233647
12.7%
n166795
 
9.1%
o166650
 
9.1%
u133255
 
7.3%
t133138
 
7.3%
a100263
 
5.5%
s100247
 
5.5%
d99676
 
5.4%
m66916
 
3.6%
r66916
 
3.6%
Other values (13)566757
30.9%

furnishing_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Fully-Furnished
66829 
Semi-Furnished
66673 
Unfurnished
66498 

Length

Max length15
Median length14
Mean length13.336675
Min length11

Characters and Unicode

Total characters2667335
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSemi-Furnished
2nd rowSemi-Furnished
3rd rowSemi-Furnished
4th rowSemi-Furnished
5th rowFully-Furnished

Common Values

ValueCountFrequency (%)
Fully-Furnished66829
33.4%
Semi-Furnished66673
33.3%
Unfurnished66498
33.2%

Length

2025-11-11T00:28:45.201138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:45.297927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fully-furnished66829
33.4%
semi-furnished66673
33.3%
unfurnished66498
33.2%

Most occurring characters

ValueCountFrequency (%)
u266829
10.0%
i266673
10.0%
e266673
10.0%
n266498
10.0%
F200331
7.5%
r200000
7.5%
s200000
7.5%
h200000
7.5%
d200000
7.5%
l133658
 
5.0%
Other values (6)466673
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2667335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u266829
10.0%
i266673
10.0%
e266673
10.0%
n266498
10.0%
F200331
7.5%
r200000
7.5%
s200000
7.5%
h200000
7.5%
d200000
7.5%
l133658
 
5.0%
Other values (6)466673
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2667335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u266829
10.0%
i266673
10.0%
e266673
10.0%
n266498
10.0%
F200331
7.5%
r200000
7.5%
s200000
7.5%
h200000
7.5%
d200000
7.5%
l133658
 
5.0%
Other values (6)466673
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2667335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u266829
10.0%
i266673
10.0%
e266673
10.0%
n266498
10.0%
F200331
7.5%
r200000
7.5%
s200000
7.5%
h200000
7.5%
d200000
7.5%
l133658
 
5.0%
Other values (6)466673
17.5%

property_size_sqft
Real number (ℝ)

High correlation 

Distinct5601
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3195.6335
Minimum400
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:45.421014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile682
Q11802
median3190
Q34589
95-th percentile5720
Maximum6000
Range5600
Interquartile range (IQR)2787

Descriptive statistics

Standard deviation1613.3223
Coefficient of variation (CV)0.50485211
Kurtosis-1.195565
Mean3195.6335
Median Absolute Deviation (MAD)1393
Skewness0.0050609308
Sum6.3912669 × 108
Variance2602808.8
MonotonicityNot monotonic
2025-11-11T00:28:45.571228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239060
 
< 0.1%
364758
 
< 0.1%
135857
 
< 0.1%
287757
 
< 0.1%
345757
 
< 0.1%
238857
 
< 0.1%
228357
 
< 0.1%
491855
 
< 0.1%
237955
 
< 0.1%
149155
 
< 0.1%
Other values (5591)199432
99.7%
ValueCountFrequency (%)
40036
< 0.1%
40130
< 0.1%
40235
< 0.1%
40343
< 0.1%
40433
< 0.1%
40527
< 0.1%
40637
< 0.1%
40735
< 0.1%
40838
< 0.1%
40936
< 0.1%
ValueCountFrequency (%)
600042
< 0.1%
599946
< 0.1%
599840
< 0.1%
599742
< 0.1%
599633
< 0.1%
599533
< 0.1%
599435
< 0.1%
599339
< 0.1%
599233
< 0.1%
599135
< 0.1%

price
Real number (ℝ)

High correlation 

Distinct192130
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1215365.1
Minimum56288
Maximum4202732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:45.711384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum56288
5-th percentile210252
Q1565989.5
median1023429
Q31725556.5
95-th percentile2807338.9
Maximum4202732
Range4146444
Interquartile range (IQR)1159567

Descriptive statistics

Standard deviation823663.26
Coefficient of variation (CV)0.67770848
Kurtosis0.55102952
Mean1215365.1
Median Absolute Deviation (MAD)547891.5
Skewness0.94489719
Sum2.4307303 × 1011
Variance6.7842116 × 1011
MonotonicityNot monotonic
2025-11-11T00:28:45.860369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4780484
 
< 0.1%
13193613
 
< 0.1%
3105113
 
< 0.1%
4902863
 
< 0.1%
3338733
 
< 0.1%
6466063
 
< 0.1%
15896423
 
< 0.1%
11904883
 
< 0.1%
6472003
 
< 0.1%
3379903
 
< 0.1%
Other values (192120)199969
> 99.9%
ValueCountFrequency (%)
562881
< 0.1%
563311
< 0.1%
563561
< 0.1%
568371
< 0.1%
570581
< 0.1%
570741
< 0.1%
571421
< 0.1%
574991
< 0.1%
577371
< 0.1%
579821
< 0.1%
ValueCountFrequency (%)
42027321
< 0.1%
42027211
< 0.1%
42021511
< 0.1%
42019121
< 0.1%
42012771
< 0.1%
42011971
< 0.1%
42009001
< 0.1%
42008941
< 0.1%
41998921
< 0.1%
41988881
< 0.1%

constructed_year
Real number (ℝ)

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.4878
Minimum1960
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:46.008733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963
Q11975
median1991
Q32008
95-th percentile2020
Maximum2023
Range63
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.494064
Coefficient of variation (CV)0.0092865565
Kurtosis-1.2044606
Mean1991.4878
Median Absolute Deviation (MAD)16
Skewness0.00024224439
Sum3.9829755 × 108
Variance342.03039
MonotonicityNot monotonic
2025-11-11T00:28:46.183769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19913239
 
1.6%
20133231
 
1.6%
19623230
 
1.6%
19713206
 
1.6%
19633206
 
1.6%
19993199
 
1.6%
20193190
 
1.6%
19813186
 
1.6%
19693185
 
1.6%
20123170
 
1.6%
Other values (54)167958
84.0%
ValueCountFrequency (%)
19603047
1.5%
19613097
1.5%
19623230
1.6%
19633206
1.6%
19643143
1.6%
19653130
1.6%
19663140
1.6%
19673169
1.6%
19683100
1.6%
19693185
1.6%
ValueCountFrequency (%)
20233135
1.6%
20223116
1.6%
20213119
1.6%
20203125
1.6%
20193190
1.6%
20183079
1.5%
20173095
1.5%
20163111
1.6%
20153111
1.6%
20143126
1.6%

previous_owners
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.00137
Minimum0
Maximum6
Zeros28648
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:46.303426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0021983
Coefficient of variation (CV)0.6670948
Kurtosis-1.253155
Mean3.00137
Median Absolute Deviation (MAD)2
Skewness-0.001676501
Sum600274
Variance4.0087982
MonotonicityNot monotonic
2025-11-11T00:28:46.400553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
528734
14.4%
028648
14.3%
628635
14.3%
128627
14.3%
328591
14.3%
428432
14.2%
228333
14.2%
ValueCountFrequency (%)
028648
14.3%
128627
14.3%
228333
14.2%
328591
14.3%
428432
14.2%
528734
14.4%
628635
14.3%
ValueCountFrequency (%)
628635
14.3%
528734
14.4%
428432
14.2%
328591
14.3%
228333
14.2%
128627
14.3%
028648
14.3%

rooms
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.513855
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:46.493943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2956686
Coefficient of variation (CV)0.50858271
Kurtosis-1.2433445
Mean4.513855
Median Absolute Deviation (MAD)2
Skewness-0.0079755019
Sum902771
Variance5.2700944
MonotonicityNot monotonic
2025-11-11T00:28:46.596458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
825359
12.7%
625228
12.6%
725182
12.6%
324983
12.5%
124929
12.5%
224847
12.4%
524797
12.4%
424675
12.3%
ValueCountFrequency (%)
124929
12.5%
224847
12.4%
324983
12.5%
424675
12.3%
524797
12.4%
625228
12.6%
725182
12.6%
825359
12.7%
ValueCountFrequency (%)
825359
12.7%
725182
12.6%
625228
12.6%
524797
12.4%
424675
12.3%
324983
12.5%
224847
12.4%
124929
12.5%

bathrooms
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.76003
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:46.684272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8409594
Coefficient of variation (CV)0.66700701
Kurtosis0.044008253
Mean2.76003
Median Absolute Deviation (MAD)1
Skewness0.95743284
Sum552006
Variance3.3891313
MonotonicityNot monotonic
2025-11-11T00:28:46.779466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
167914
34.0%
242602
21.3%
330335
15.2%
422274
 
11.1%
515827
 
7.9%
610944
 
5.5%
76844
 
3.4%
83260
 
1.6%
ValueCountFrequency (%)
167914
34.0%
242602
21.3%
330335
15.2%
422274
 
11.1%
515827
 
7.9%
610944
 
5.5%
76844
 
3.4%
83260
 
1.6%
ValueCountFrequency (%)
83260
 
1.6%
76844
 
3.4%
610944
 
5.5%
515827
 
7.9%
422274
 
11.1%
330335
15.2%
242602
21.3%
167914
34.0%

garage
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
100130 
1
99870 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0100130
50.1%
199870
49.9%

Length

2025-11-11T00:28:46.900833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:46.977185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0100130
50.1%
199870
49.9%

Most occurring characters

ValueCountFrequency (%)
0100130
50.1%
199870
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0100130
50.1%
199870
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0100130
50.1%
199870
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0100130
50.1%
199870
49.9%

garden
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
100043 
0
99957 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1100043
50.0%
099957
50.0%

Length

2025-11-11T00:28:47.071680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:47.146933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1100043
50.0%
099957
50.0%

Most occurring characters

ValueCountFrequency (%)
1100043
50.0%
099957
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1100043
50.0%
099957
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1100043
50.0%
099957
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1100043
50.0%
099957
50.0%

crime_cases_reported
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.229065
Minimum0
Maximum10
Zeros62909
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:47.225471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1853359
Coefficient of variation (CV)0.96442086
Kurtosis1.4721829
Mean1.229065
Median Absolute Deviation (MAD)1
Skewness1.1055183
Sum245813
Variance1.4050213
MonotonicityNot monotonic
2025-11-11T00:28:47.326460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
169217
34.6%
062909
31.5%
240620
20.3%
317762
 
8.9%
46446
 
3.2%
52251
 
1.1%
6591
 
0.3%
7160
 
0.1%
833
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
062909
31.5%
169217
34.6%
240620
20.3%
317762
 
8.9%
46446
 
3.2%
52251
 
1.1%
6591
 
0.3%
7160
 
0.1%
833
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
99
 
< 0.1%
833
 
< 0.1%
7160
 
0.1%
6591
 
0.3%
52251
 
1.1%
46446
 
3.2%
317762
 
8.9%
240620
20.3%
169217
34.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
150216 
1
49784 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0150216
75.1%
149784
 
24.9%

Length

2025-11-11T00:28:47.443409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:47.513352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0150216
75.1%
149784
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0150216
75.1%
149784
 
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0150216
75.1%
149784
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0150216
75.1%
149784
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0150216
75.1%
149784
 
24.9%

customer_salary
Real number (ℝ)

High correlation 

Distinct36020
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46528.626
Minimum2000
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:47.624332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile8779.9
Q121450
median41465
Q370805
95-th percentile94180
Maximum100000
Range98000
Interquartile range (IQR)49355

Descriptive statistics

Standard deviation27997.354
Coefficient of variation (CV)0.6017232
Kurtosis-1.1808382
Mean46528.626
Median Absolute Deviation (MAD)23401
Skewness0.29542866
Sum9.3057252 × 109
Variance7.8385182 × 108
MonotonicityNot monotonic
2025-11-11T00:28:47.780854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1316027
 
< 0.1%
1140023
 
< 0.1%
4284022
 
< 0.1%
2279522
 
< 0.1%
1720021
 
< 0.1%
1430521
 
< 0.1%
6524521
 
< 0.1%
6847521
 
< 0.1%
3256521
 
< 0.1%
2208021
 
< 0.1%
Other values (36010)199780
99.9%
ValueCountFrequency (%)
20001
 
< 0.1%
20061
 
< 0.1%
20073
< 0.1%
20083
< 0.1%
20092
< 0.1%
20112
< 0.1%
20122
< 0.1%
20141
 
< 0.1%
20181
 
< 0.1%
20191
 
< 0.1%
ValueCountFrequency (%)
10000012
< 0.1%
9999516
< 0.1%
9999011
< 0.1%
9998514
< 0.1%
999804
 
< 0.1%
9997511
< 0.1%
9997010
< 0.1%
9996511
< 0.1%
9996010
< 0.1%
999558
< 0.1%

loan_amount
Real number (ℝ)

High correlation 

Distinct187684
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean759758.28
Minimum23504
Maximum3520150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:47.940304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23504
5-th percentile124582.7
Q1337280.25
median626932.5
Q31058416
95-th percentile1826092.1
Maximum3520150
Range3496646
Interquartile range (IQR)721135.75

Descriptive statistics

Standard deviation548940.15
Coefficient of variation (CV)0.72251947
Kurtosis1.5035704
Mean759758.28
Median Absolute Deviation (MAD)336338
Skewness1.1912318
Sum1.5195166 × 1011
Variance3.0133529 × 1011
MonotonicityNot monotonic
2025-11-11T00:28:48.110803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1924405
 
< 0.1%
2514444
 
< 0.1%
3850724
 
< 0.1%
3739314
 
< 0.1%
4315194
 
< 0.1%
5778584
 
< 0.1%
5728424
 
< 0.1%
1673914
 
< 0.1%
3734414
 
< 0.1%
2463364
 
< 0.1%
Other values (187674)199959
> 99.9%
ValueCountFrequency (%)
235041
< 0.1%
253751
< 0.1%
256431
< 0.1%
257241
< 0.1%
258041
< 0.1%
258081
< 0.1%
259951
< 0.1%
263821
< 0.1%
267731
< 0.1%
269011
< 0.1%
ValueCountFrequency (%)
35201501
< 0.1%
35201211
< 0.1%
35151411
< 0.1%
34950561
< 0.1%
34950271
< 0.1%
34925601
< 0.1%
34816071
< 0.1%
34794551
< 0.1%
34785601
< 0.1%
34765491
< 0.1%

loan_tenure_years
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
10
40207 
15
40147 
30
40071 
25
39874 
20
39701 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters400000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row20
3rd row30
4th row15
5th row25

Common Values

ValueCountFrequency (%)
1040207
20.1%
1540147
20.1%
3040071
20.0%
2539874
19.9%
2039701
19.9%

Length

2025-11-11T00:28:48.261485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:48.360997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1040207
20.1%
1540147
20.1%
3040071
20.0%
2539874
19.9%
2039701
19.9%

Most occurring characters

ValueCountFrequency (%)
0119979
30.0%
180354
20.1%
580021
20.0%
279575
19.9%
340071
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0119979
30.0%
180354
20.1%
580021
20.0%
279575
19.9%
340071
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0119979
30.0%
180354
20.1%
580021
20.0%
279575
19.9%
340071
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0119979
30.0%
180354
20.1%
580021
20.0%
279575
19.9%
340071
 
10.0%

monthly_expenses
Real number (ℝ)

Distinct6655
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10559.693
Minimum500
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:48.586964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2580
Q15770
median10520
Q315260
95-th percentile19065
Maximum20000
Range19500
Interquartile range (IQR)9490

Descriptive statistics

Standard deviation5427.2419
Coefficient of variation (CV)0.5139583
Kurtosis-1.2081943
Mean10559.693
Median Absolute Deviation (MAD)4745
Skewness0.020596748
Sum2.1119386 × 109
Variance29454954
MonotonicityNot monotonic
2025-11-11T00:28:48.829818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1751082
 
< 0.1%
383080
 
< 0.1%
309576
 
< 0.1%
1973076
 
< 0.1%
282075
 
< 0.1%
364075
 
< 0.1%
1899575
 
< 0.1%
393074
 
< 0.1%
1125073
 
< 0.1%
1262073
 
< 0.1%
Other values (6645)199241
99.6%
ValueCountFrequency (%)
5005
< 0.1%
5012
 
< 0.1%
5025
< 0.1%
5033
< 0.1%
5044
< 0.1%
5055
< 0.1%
5062
 
< 0.1%
5073
< 0.1%
5087
< 0.1%
5096
< 0.1%
ValueCountFrequency (%)
2000054
< 0.1%
1999547
< 0.1%
1999056
< 0.1%
1998552
< 0.1%
1998043
< 0.1%
1997559
< 0.1%
1997055
< 0.1%
1996558
< 0.1%
1996052
< 0.1%
1995557
< 0.1%

down_payment
Real number (ℝ)

High correlation 

Distinct180244
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455606.85
Minimum8966
Maximum2492723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:49.085386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8966
5-th percentile66019.85
Q1184959.25
median356170
Q3625735.25
95-th percentile1182212
Maximum2492723
Range2483757
Interquartile range (IQR)440776

Descriptive statistics

Standard deviation362986.52
Coefficient of variation (CV)0.79670996
Kurtosis2.4799512
Mean455606.85
Median Absolute Deviation (MAD)199817.5
Skewness1.453053
Sum9.112137 × 1010
Variance1.3175921 × 1011
MonotonicityNot monotonic
2025-11-11T00:28:49.339649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009165
 
< 0.1%
1977825
 
< 0.1%
1070245
 
< 0.1%
750415
 
< 0.1%
1205205
 
< 0.1%
1293615
 
< 0.1%
2045374
 
< 0.1%
1824814
 
< 0.1%
2134034
 
< 0.1%
2710134
 
< 0.1%
Other values (180234)199954
> 99.9%
ValueCountFrequency (%)
89661
< 0.1%
99961
< 0.1%
102651
< 0.1%
107951
< 0.1%
108421
< 0.1%
108811
< 0.1%
109961
< 0.1%
110881
< 0.1%
111241
< 0.1%
111751
< 0.1%
ValueCountFrequency (%)
24927231
< 0.1%
24662581
< 0.1%
24649401
< 0.1%
24577031
< 0.1%
24534181
< 0.1%
24496741
< 0.1%
24468561
< 0.1%
24464731
< 0.1%
24378241
< 0.1%
24313161
< 0.1%

emi_to_income_ratio
Real number (ℝ)

High correlation 

Distinct266
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1953873
Minimum0
Maximum3.46
Zeros25
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:49.550108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.07
median0.13
Q30.24
95-th percentile0.61
Maximum3.46
Range3.46
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.21968866
Coefficient of variation (CV)1.1243754
Kurtosis17.248462
Mean0.1953873
Median Absolute Deviation (MAD)0.07
Skewness3.3032618
Sum39077.46
Variance0.048263109
MonotonicityNot monotonic
2025-11-11T00:28:49.764218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.069626
 
4.8%
0.059476
 
4.7%
0.079324
 
4.7%
0.049238
 
4.6%
0.089167
 
4.6%
0.038943
 
4.5%
0.098644
 
4.3%
0.18138
 
4.1%
0.027825
 
3.9%
0.117647
 
3.8%
Other values (256)111972
56.0%
ValueCountFrequency (%)
025
 
< 0.1%
0.013723
 
1.9%
0.027825
3.9%
0.038943
4.5%
0.049238
4.6%
0.059476
4.7%
0.069626
4.8%
0.079324
4.7%
0.089167
4.6%
0.098644
4.3%
ValueCountFrequency (%)
3.461
< 0.1%
3.381
< 0.1%
3.311
< 0.1%
3.31
< 0.1%
3.261
< 0.1%
3.171
< 0.1%
3.081
< 0.1%
3.031
< 0.1%
2.971
< 0.1%
2.941
< 0.1%

satisfaction_score
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.49865
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:49.936667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8753608
Coefficient of variation (CV)0.52292122
Kurtosis-1.2253934
Mean5.49865
Median Absolute Deviation (MAD)3
Skewness0.00066491434
Sum1099730
Variance8.2676995
MonotonicityNot monotonic
2025-11-11T00:28:50.072184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
120171
10.1%
1020071
10.0%
420068
10.0%
920051
10.0%
520045
10.0%
319941
10.0%
819937
10.0%
619908
10.0%
219906
10.0%
719902
10.0%
ValueCountFrequency (%)
120171
10.1%
219906
10.0%
319941
10.0%
420068
10.0%
520045
10.0%
619908
10.0%
719902
10.0%
819937
10.0%
920051
10.0%
1020071
10.0%
ValueCountFrequency (%)
1020071
10.0%
920051
10.0%
819937
10.0%
719902
10.0%
619908
10.0%
520045
10.0%
420068
10.0%
319941
10.0%
219906
10.0%
120171
10.1%

neighbourhood_rating
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.50524
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:50.209273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8709447
Coefficient of variation (CV)0.52149311
Kurtosis-1.2262416
Mean5.50524
Median Absolute Deviation (MAD)2
Skewness-0.00015314375
Sum1101048
Variance8.2423238
MonotonicityNot monotonic
2025-11-11T00:28:50.369619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
320323
10.2%
920245
10.1%
420127
10.1%
820088
10.0%
520020
10.0%
1019941
10.0%
719937
10.0%
119829
9.9%
219794
9.9%
619696
9.8%
ValueCountFrequency (%)
119829
9.9%
219794
9.9%
320323
10.2%
420127
10.1%
520020
10.0%
619696
9.8%
719937
10.0%
820088
10.0%
920245
10.1%
1019941
10.0%
ValueCountFrequency (%)
1019941
10.0%
920245
10.1%
820088
10.0%
719937
10.0%
619696
9.8%
520020
10.0%
420127
10.1%
320323
10.2%
219794
9.9%
119829
9.9%

connectivity_score
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.495615
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-11T00:28:50.504470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8702059
Coefficient of variation (CV)0.52227201
Kurtosis-1.2243246
Mean5.495615
Median Absolute Deviation (MAD)2
Skewness0.0024840839
Sum1099123
Variance8.238082
MonotonicityNot monotonic
2025-11-11T00:28:50.650576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
320185
10.1%
820134
10.1%
420087
10.0%
220012
10.0%
720012
10.0%
519969
10.0%
119936
10.0%
919903
10.0%
1019890
9.9%
619872
9.9%
ValueCountFrequency (%)
119936
10.0%
220012
10.0%
320185
10.1%
420087
10.0%
519969
10.0%
619872
9.9%
720012
10.0%
820134
10.1%
919903
10.0%
1019890
9.9%
ValueCountFrequency (%)
1019890
9.9%
919903
10.0%
820134
10.1%
720012
10.0%
619872
9.9%
519969
10.0%
420087
10.0%
320185
10.1%
220012
10.0%
119936
10.0%

decision
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
153932 
1
46068 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0153932
77.0%
146068
 
23.0%

Length

2025-11-11T00:28:50.842182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T00:28:50.951071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0153932
77.0%
146068
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0153932
77.0%
146068
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0153932
77.0%
146068
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0153932
77.0%
146068
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0153932
77.0%
146068
 
23.0%

Interactions

2025-11-11T00:28:39.567512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:27:59.645388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:02.283104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:04.547451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:06.882801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:09.565503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:12.796305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.093471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:17.403250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:19.912458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:22.392919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:25.629622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:28.555513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.000909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:33.637707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:36.459186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:40.180686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:27:59.827278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:02.452985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:04.684298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.048390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:09.783660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:12.953771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.244620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:17.554003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.062859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:22.625619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:25.782599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:28.700500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.172407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:33.795304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:36.677043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:40.356783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:27:59.969092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:02.592886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:04.821127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.180704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:09.988287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.093025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.384591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:17.697116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.194545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:22.853424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:25.934373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:28.841644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.373257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:33.937110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:36.877942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:40.500523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:00.123597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:02.722820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:04.963698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.314133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:10.193139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.229425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.538017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:17.851973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.328341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:23.065470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:26.098663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:28.995011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.526900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:34.073698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:37.078918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:40.651998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:00.274516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:02.884391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:05.105021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.453530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:10.395486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.369810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.679580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:17.992800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.463141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:23.279669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:26.253974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:29.160381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.679943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:34.234050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:37.346321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:40.797561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:00.431202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:03.013658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:05.244141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.595038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:10.587256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.519830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.820711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:18.128612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.602644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:23.500677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:26.429839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:29.317078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.821363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:34.373880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:37.583513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:40.955896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:00.579094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:03.147562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:05.377831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.735013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:10.800328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.665570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:15.956096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:18.260253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.754603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:23.727968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:26.579845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:29.475348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:31.986028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:34.537940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:37.841366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:41.106636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:00.753952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:03.294944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:05.511411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:07.886717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:11.024063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.806872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:16.102004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:18.395873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:20.899460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:23.953952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:26.739586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:29.629515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:32.150491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:34.671520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:38.073839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:41.248711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:00.905095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:03.433294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:05.642773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:08.033488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:11.264027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:13.944637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:16.235392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:18.527439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:21.034821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:24.172930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:26.893688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:29.764700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:32.334861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:34.803166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:38.303442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:41.398142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:01.051313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:03.569885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:05.770189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:08.172258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:11.501972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:14.090057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T00:28:41.552832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:01.196608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T00:28:16.523189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T00:28:36.047986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T00:28:09.363168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:12.663654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:14.966121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:17.273094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:19.781101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:22.200859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:25.489279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:28.411318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:30.842692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:33.475463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:36.257858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T00:28:39.425627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T00:28:51.127671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bathroomscityconnectivity_scoreconstructed_yearcountrycrime_cases_reportedcustomer_salarydecisiondown_paymentemi_to_income_ratiofurnishing_statusgaragegardenlegal_cases_on_propertyloan_amountloan_tenure_yearsmonthly_expensesneighbourhood_ratingprevious_ownerspriceproperty_idproperty_size_sqftproperty_typeroomssatisfaction_score
bathrooms1.0000.004-0.0020.0030.0040.003-0.0010.000-0.003-0.0000.0020.0040.0040.000-0.0020.000-0.0000.002-0.000-0.002-0.001-0.0000.0020.6250.001
city0.0041.0000.0060.0001.0000.1160.2410.0760.1940.0670.0000.0040.0040.0020.2210.0000.2670.0040.0000.2740.0000.0020.0030.0030.000
connectivity_score-0.0020.0061.0000.0030.003-0.0030.0030.0000.0020.0010.0060.0060.0000.0000.0020.000-0.002-0.0000.0040.002-0.0030.0020.000-0.003-0.000
constructed_year0.0030.0000.0031.0000.0020.0020.0000.005-0.001-0.0050.0030.0040.0030.010-0.0050.000-0.003-0.0020.002-0.0040.000-0.0060.000-0.002-0.003
country0.0041.0000.0030.0021.0000.1160.2410.0750.1940.0670.0030.0000.0000.0000.2210.0000.2670.0010.0000.2740.0000.0000.0000.0010.000
crime_cases_reported0.0030.116-0.0030.0020.1161.000-0.0880.217-0.0590.0130.0050.0000.0000.000-0.0620.0000.0230.0010.001-0.0640.001-0.0010.0020.002-0.003
customer_salary-0.0010.2410.0030.0000.241-0.0881.0000.1020.249-0.5400.0030.0000.0030.0000.2710.0030.1790.0010.0010.2810.0010.0000.002-0.001-0.005
decision0.0000.0760.0000.0050.0750.2170.1021.0000.0190.1760.0000.0000.0000.3150.0500.0250.0070.0000.0000.0470.0000.0580.0000.0000.670
down_payment-0.0030.1940.002-0.0010.194-0.0590.2490.0191.0000.3900.0000.0000.0000.0060.7320.0050.120-0.0010.0010.888-0.0050.7080.000-0.0060.000
emi_to_income_ratio-0.0000.0670.001-0.0050.0670.013-0.5400.1760.3901.0000.0000.0000.0000.0000.5820.079-0.064-0.002-0.0020.542-0.0010.6420.000-0.0010.002
furnishing_status0.0020.0000.0060.0030.0030.0050.0030.0000.0000.0001.0000.0020.0060.0030.0000.0010.0010.0000.0000.0000.0000.0000.0030.0030.000
garage0.0040.0040.0060.0040.0000.0000.0000.0000.0000.0000.0021.0000.0000.0010.0040.0030.0010.0000.0040.0010.0020.0000.0060.0000.000
garden0.0040.0040.0000.0030.0000.0000.0030.0000.0000.0000.0060.0001.0000.0000.0000.0040.0050.0000.0000.0010.0020.0040.0020.0000.000
legal_cases_on_property0.0000.0020.0000.0100.0000.0000.0000.3150.0060.0000.0030.0010.0001.0000.0000.0000.0000.0030.0030.0000.0000.0000.0000.0060.003
loan_amount-0.0020.2210.002-0.0050.221-0.0620.2710.0500.7320.5820.0000.0040.0000.0001.0000.0020.127-0.0010.0000.960-0.0010.7630.000-0.002-0.001
loan_tenure_years0.0000.0000.0000.0000.0000.0000.0030.0250.0050.0790.0010.0030.0040.0000.0021.0000.0020.0000.0000.0010.0000.0020.0010.0020.002
monthly_expenses-0.0000.267-0.002-0.0030.2670.0230.1790.0070.120-0.0640.0010.0010.0050.0000.1270.0021.0000.0000.0030.1320.0000.0020.000-0.002-0.003
neighbourhood_rating0.0020.004-0.000-0.0020.0010.0010.0010.000-0.001-0.0020.0000.0000.0000.003-0.0010.0000.0001.0000.003-0.000-0.002-0.0010.002-0.0030.001
previous_owners-0.0000.0000.0040.0020.0000.0010.0010.0000.001-0.0020.0000.0040.0000.0030.0000.0000.0030.0031.0000.001-0.002-0.0000.003-0.002-0.001
price-0.0020.2740.002-0.0040.274-0.0640.2810.0470.8880.5420.0000.0010.0010.0000.9600.0010.132-0.0000.0011.000-0.0030.7930.000-0.004-0.000
property_id-0.0010.000-0.0030.0000.0000.0010.0010.000-0.005-0.0010.0000.0020.0020.000-0.0010.0000.000-0.002-0.002-0.0031.000-0.0030.002-0.003-0.000
property_size_sqft-0.0000.0020.002-0.0060.000-0.0010.0000.0580.7080.6420.0000.0000.0040.0000.7630.0020.002-0.001-0.0000.793-0.0031.0000.000-0.0020.000
property_type0.0020.0030.0000.0000.0000.0020.0020.0000.0000.0000.0030.0060.0020.0000.0000.0010.0000.0020.0030.0000.0020.0001.0000.0040.000
rooms0.6250.003-0.003-0.0020.0010.002-0.0010.000-0.006-0.0010.0030.0000.0000.006-0.0020.002-0.002-0.003-0.002-0.004-0.003-0.0020.0041.000-0.001
satisfaction_score0.0010.000-0.000-0.0030.000-0.003-0.0050.6700.0000.0020.0000.0000.0000.003-0.0010.002-0.0030.001-0.001-0.000-0.0000.0000.000-0.0011.000

Missing values

2025-11-11T00:28:42.615781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T00:28:43.215403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

property_idcountrycityproperty_typefurnishing_statusproperty_size_sqftpriceconstructed_yearprevious_ownersroomsbathroomsgaragegardencrime_cases_reportedlegal_cases_on_propertycustomer_salaryloan_amountloan_tenure_yearsmonthly_expensesdown_paymentemi_to_income_ratiosatisfaction_scoreneighbourhood_ratingconnectivity_scoredecision
01FranceMarseilleFarmhouseSemi-Furnished99141293519896621110107451939491565452189860.161560
12South AfricaCape TownApartmentSemi-Furnished12442245381990488111116970181465208605430730.089120
23South AfricaJohannesburgFarmhouseSemi-Furnished415274510420195211100219143079533025104371510.096810
34GermanyFrankfurtFarmhouseSemi-Furnished3714111095920081330100179806747201588054362390.332660
45South AfricaJohannesburgTownhouseFully-Furnished53199041200763311311767665833258965332080.033340
56CanadaMontrealVillaSemi-Furnished31691107368198505210009552079331630106153140520.0510821
67BrazilRio de JaneiroStudioUnfurnished1986398439197612110001142626816725144401302720.16910101
78BrazilSão PauloTownhouseSemi-Furnished404880723620204661110298325033852072003038510.131580
89UAEDubaiFarmhouseSemi-Furnished5213313137319686210000562551866416151030012649570.2988101
910AustraliaMelbourneApartmentUnfurnished464814832501966252010167350109329010163603899600.181470
property_idcountrycityproperty_typefurnishing_statusproperty_size_sqftpriceconstructed_yearprevious_ownersroomsbathroomsgaragegardencrime_cases_reportedlegal_cases_on_propertycustomer_salaryloan_amountloan_tenure_yearsmonthly_expensesdown_paymentemi_to_income_ratiosatisfaction_scoreneighbourhood_ratingconnectivity_scoredecision
199990199991USANew YorkTownhouseFully-Furnished758378580201047410401855521985030104901587300.074330
199991199992SingaporeSingaporeApartmentSemi-Furnished21781525303201065210001201011679403062053573630.619390
199992199993JapanKyotoFarmhouseFully-Furnished555324977481965043000028865154788920153009498590.403960
199993199994AustraliaMelbourneTownhouseSemi-Furnished35541141359200856600109642547080910177906705500.063390
199994199995JapanOsakaFarmhouseFully-Furnished5321239691019633861010178251056582201109513403280.4491020
199995199996GermanyBerlinVillaFully-Furnished68520332819681320010783301040501517670992780.018451
199996199997ChinaShenzhenTownhouseUnfurnished38181454627197757511102540011752972028652793300.3471091
199997199998JapanKyotoVillaSemi-Furnished3603161914719902441110282207430493055958760980.175390
199998199999South AfricaJohannesburgApartmentUnfurnished1706306165201004110011224015077415163001553910.1161060
199999200000BrazilRio de JaneiroApartmentSemi-Furnished365273269819860111030226445487143051651839840.156490